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# copyright ownership.  The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
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import pytest
import numpy
import numpy.linalg as NLA

import modin.pandas as pd
import modin.numpy as np
import modin.numpy.linalg as LA
from .utils import assert_scalar_or_array_equal


def test_dot_from_pandas_reindex():
    # Reindexing the dataframe does not change the output of dot
    # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dot.html
    df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
    s = pd.Series([1, 1, 2, 1])
    result1 = np.dot(df, s)
    s2 = s.reindex([1, 0, 2, 3])
    result2 = np.dot(df, s2)
    assert_scalar_or_array_equal(result1, result2)


def test_dot_1d():
    x1 = numpy.random.randint(-100, 100, size=100)
    x2 = numpy.random.randint(-100, 100, size=100)
    numpy_result = numpy.dot(x1, x2)
    x1, x2 = np.array(x1), np.array(x2)
    modin_result = np.dot(x1, x2)
    assert_scalar_or_array_equal(modin_result, numpy_result)


def test_dot_2d():
    x1 = numpy.random.randint(-100, 100, size=(100, 3))
    x2 = numpy.random.randint(-100, 100, size=(3, 50))
    numpy_result = numpy.dot(x1, x2)
    x1, x2 = np.array(x1), np.array(x2)
    modin_result = np.dot(x1, x2)
    assert_scalar_or_array_equal(modin_result, numpy_result)


def test_dot_scalar():
    x1 = numpy.random.randint(-100, 100, size=(100, 3))
    x2 = numpy.random.randint(-100, 100)
    numpy_result = numpy.dot(x1, x2)
    x1 = np.array(x1)
    modin_result = np.dot(x1, x2)
    assert_scalar_or_array_equal(modin_result, numpy_result)


def test_matmul_scalar():
    x1 = numpy.random.randint(-100, 100, size=(100, 3))
    x2 = numpy.random.randint(-100, 100)
    x1 = np.array(x1)
    # Modin error message differs from numpy for readability; the original numpy error is:
    # ValueError: matmul: Input operand 1 does not have enough dimensions (has 0, gufunc
    # core with signature (n?,k),(k,m?)->(n?,m?) requires 1)
    with pytest.raises(ValueError):
        x1 @ x2


def test_dot_broadcast():
    # 2D @ 1D
    x1 = numpy.random.randint(-100, 100, size=(100, 3))
    x2 = numpy.random.randint(-100, 100, size=(3,))
    numpy_result = numpy.dot(x1, x2)
    x1, x2 = np.array(x1), np.array(x2)
    modin_result = np.dot(x1, x2)
    assert_scalar_or_array_equal(modin_result, numpy_result)

    # 1D @ 2D
    x1 = numpy.random.randint(-100, 100, size=(100,))
    x2 = numpy.random.randint(-100, 100, size=(100, 3))
    numpy_result = numpy.dot(x1, x2)
    x1, x2 = np.array(x1), np.array(x2)
    modin_result = np.dot(x1, x2)
    assert_scalar_or_array_equal(modin_result, numpy_result)


@pytest.mark.parametrize("axis", [None, 0, 1], ids=["axis=None", "axis=0", "axis=1"])
def test_norm_fro_2d(axis):
    x1 = numpy.random.randint(-10, 10, size=(100, 3))
    numpy_result = NLA.norm(x1, axis=axis)
    x1 = np.array(x1)
    modin_result = LA.norm(x1, axis=axis)
    # Result may be a scalar
    if isinstance(modin_result, np.array):
        modin_result = modin_result._to_numpy()
    numpy.testing.assert_allclose(modin_result, numpy_result, rtol=1e-12)


def test_norm_fro_1d():
    x1 = numpy.random.randint(-10, 10, size=100)
    numpy_result = NLA.norm(x1)
    x1 = np.array(x1)
    modin_result = LA.norm(x1)
    numpy.testing.assert_allclose(modin_result, numpy_result, rtol=1e-12)
